Leveraging a Data Fabric for Generative AI

The introduction of generative AI has transformed the artificial intelligence landscape. Use cases for generative AI now extend beyond simple chatbots and employee onboarding systems to implementations requiring more sensitive business data, such as customer or product data. For example, an agent could be enhanced by incorporating customer loyalty information to tailor its responses based on the person it is conversing with.

To effectively implement this type of AI, a solid data foundation is essential. A hybrid data environment further complicates the process of building generative AI models with business data. How can you find relevant data to answer a question when it is scattered across multiple systems, stored in different formats, and requires varied access methods? How can you ensure that the data fed to a generative AI model is accurate and up-to-date?

This TDWI Checklist report explores how to implement generative AI applications by adopting a logical architectural approach, such as a data fabric, combined with techniques that enable the secure integration of internal enterprise data.